Overview

Dataset statistics

Number of variables22
Number of observations130
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.5 KiB
Average record size in memory177.0 B

Variable types

Text1
Categorical4
Numeric17

Alerts

PLAYER NAME has unique valuesUnique
T-RUNS has 28 (21.5%) zerosZeros
T-WKTS has 45 (34.6%) zerosZeros
ODI-RUNS-S has 9 (6.9%) zerosZeros
ODI-SR-B has 9 (6.9%) zerosZeros
ODI-WKTS has 34 (26.2%) zerosZeros
ODI-SR-BL has 34 (26.2%) zerosZeros
RUNS-S has 2 (1.5%) zerosZeros
HS has 2 (1.5%) zerosZeros
AVE has 3 (2.3%) zerosZeros
SR-B has 2 (1.5%) zerosZeros
SIXERS has 26 (20.0%) zerosZeros
RUNS-C has 36 (27.7%) zerosZeros
WKTS has 41 (31.5%) zerosZeros
AVE-BL has 41 (31.5%) zerosZeros
ECON has 36 (27.7%) zerosZeros
SR-BL has 41 (31.5%) zerosZeros

Reproduction

Analysis started2024-03-19 14:07:22.001651
Analysis finished2024-03-19 14:07:44.966594
Duration22.96 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

PLAYER NAME
Text

UNIQUE 

Distinct130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:45.107608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length10.584615
Min length6

Characters and Unicode

Total characters1376
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)100.0%

Sample

1st rowAbdulla, YA
2nd rowAbdur Razzak
3rd rowAgarkar, AB
4th rowAshwin, R
5th rowBadrinath, S
ValueCountFrequency (%)
a 6
 
2.3%
m 5
 
1.9%
s 5
 
1.9%
ab 4
 
1.5%
singh 4
 
1.5%
sharma 3
 
1.1%
khan 3
 
1.1%
ojha 2
 
0.8%
de 2
 
0.8%
pp 2
 
0.8%
Other values (208) 227
86.3%
2024-03-19T19:37:45.374746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 142
 
10.3%
133
 
9.7%
, 114
 
8.3%
r 66
 
4.8%
i 62
 
4.5%
n 60
 
4.4%
e 55
 
4.0%
h 54
 
3.9%
S 44
 
3.2%
l 41
 
3.0%
Other values (43) 605
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 765
55.6%
Uppercase Letter 361
26.2%
Space Separator 133
 
9.7%
Other Punctuation 114
 
8.3%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 142
18.6%
r 66
 
8.6%
i 62
 
8.1%
n 60
 
7.8%
e 55
 
7.2%
h 54
 
7.1%
l 41
 
5.4%
o 34
 
4.4%
s 31
 
4.1%
t 29
 
3.8%
Other values (16) 191
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 44
12.2%
M 36
 
10.0%
A 31
 
8.6%
P 27
 
7.5%
D 25
 
6.9%
K 24
 
6.6%
R 22
 
6.1%
J 18
 
5.0%
B 16
 
4.4%
H 15
 
4.2%
Other values (14) 103
28.5%
Space Separator
ValueCountFrequency (%)
133
100.0%
Other Punctuation
ValueCountFrequency (%)
, 114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1126
81.8%
Common 250
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 142
 
12.6%
r 66
 
5.9%
i 62
 
5.5%
n 60
 
5.3%
e 55
 
4.9%
h 54
 
4.8%
S 44
 
3.9%
l 41
 
3.6%
M 36
 
3.2%
o 34
 
3.0%
Other values (40) 532
47.2%
Common
ValueCountFrequency (%)
133
53.2%
, 114
45.6%
- 3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 142
 
10.3%
133
 
9.7%
, 114
 
8.3%
r 66
 
4.8%
i 62
 
4.5%
n 60
 
4.4%
e 55
 
4.0%
h 54
 
3.9%
S 44
 
3.2%
l 41
 
3.0%
Other values (43) 605
44.0%

AGE
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
86 
3
28 
1
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 86
66.2%
3 28
 
21.5%
1 16
 
12.3%

Length

2024-03-19T19:37:45.987098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-19T19:37:46.065520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 86
66.2%
3 28
 
21.5%
1 16
 
12.3%

Most occurring characters

ValueCountFrequency (%)
2 86
66.2%
3 28
 
21.5%
1 16
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 86
66.2%
3 28
 
21.5%
1 16
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 86
66.2%
3 28
 
21.5%
1 16
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 86
66.2%
3 28
 
21.5%
1 16
 
12.3%

COUNTRY
Categorical

Distinct10
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
IND
53 
AUS
22 
SA
16 
SL
12 
PAK
Other values (5)
18 

Length

Max length3
Median length3
Mean length2.6846154
Min length2

Characters and Unicode

Total characters349
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st rowSA
2nd rowBAN
3rd rowIND
4th rowIND
5th rowIND

Common Values

ValueCountFrequency (%)
IND 53
40.8%
AUS 22
16.9%
SA 16
 
12.3%
SL 12
 
9.2%
PAK 9
 
6.9%
NZ 7
 
5.4%
WI 6
 
4.6%
ENG 3
 
2.3%
BAN 1
 
0.8%
ZIM 1
 
0.8%

Length

2024-03-19T19:37:46.143650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-19T19:37:46.238032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ind 53
40.8%
aus 22
16.9%
sa 16
 
12.3%
sl 12
 
9.2%
pak 9
 
6.9%
nz 7
 
5.4%
wi 6
 
4.6%
eng 3
 
2.3%
ban 1
 
0.8%
zim 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
N 64
18.3%
I 60
17.2%
D 53
15.2%
S 50
14.3%
A 48
13.8%
U 22
 
6.3%
L 12
 
3.4%
P 9
 
2.6%
K 9
 
2.6%
Z 8
 
2.3%
Other values (5) 14
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 349
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 64
18.3%
I 60
17.2%
D 53
15.2%
S 50
14.3%
A 48
13.8%
U 22
 
6.3%
L 12
 
3.4%
P 9
 
2.6%
K 9
 
2.6%
Z 8
 
2.3%
Other values (5) 14
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 349
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 64
18.3%
I 60
17.2%
D 53
15.2%
S 50
14.3%
A 48
13.8%
U 22
 
6.3%
L 12
 
3.4%
P 9
 
2.6%
K 9
 
2.6%
Z 8
 
2.3%
Other values (5) 14
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 64
18.3%
I 60
17.2%
D 53
15.2%
S 50
14.3%
A 48
13.8%
U 22
 
6.3%
L 12
 
3.4%
P 9
 
2.6%
K 9
 
2.6%
Z 8
 
2.3%
Other values (5) 14
 
4.0%

PLAYING ROLE
Categorical

Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Bowler
44 
Batsman
39 
Allrounder
35 
W. Keeper
12 

Length

Max length10
Median length9
Mean length7.6538462
Min length6

Characters and Unicode

Total characters995
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllrounder
2nd rowBowler
3rd rowBowler
4th rowBowler
5th rowBatsman

Common Values

ValueCountFrequency (%)
Bowler 44
33.8%
Batsman 39
30.0%
Allrounder 35
26.9%
W. Keeper 12
 
9.2%

Length

2024-03-19T19:37:46.348179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-19T19:37:46.442549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bowler 44
31.0%
batsman 39
27.5%
allrounder 35
24.6%
w 12
 
8.5%
keeper 12
 
8.5%

Most occurring characters

ValueCountFrequency (%)
r 126
12.7%
e 115
11.6%
l 114
11.5%
B 83
8.3%
o 79
 
7.9%
a 78
 
7.8%
n 74
 
7.4%
w 44
 
4.4%
m 39
 
3.9%
s 39
 
3.9%
Other values (9) 204
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 829
83.3%
Uppercase Letter 142
 
14.3%
Other Punctuation 12
 
1.2%
Space Separator 12
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 126
15.2%
e 115
13.9%
l 114
13.8%
o 79
9.5%
a 78
9.4%
n 74
8.9%
w 44
 
5.3%
m 39
 
4.7%
s 39
 
4.7%
t 39
 
4.7%
Other values (3) 82
9.9%
Uppercase Letter
ValueCountFrequency (%)
B 83
58.5%
A 35
24.6%
W 12
 
8.5%
K 12
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 971
97.6%
Common 24
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 126
13.0%
e 115
11.8%
l 114
11.7%
B 83
8.5%
o 79
8.1%
a 78
8.0%
n 74
7.6%
w 44
 
4.5%
m 39
 
4.0%
s 39
 
4.0%
Other values (7) 180
18.5%
Common
ValueCountFrequency (%)
. 12
50.0%
12
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 126
12.7%
e 115
11.6%
l 114
11.5%
B 83
8.3%
o 79
 
7.9%
a 78
 
7.8%
n 74
 
7.4%
w 44
 
4.4%
m 39
 
3.9%
s 39
 
3.9%
Other values (9) 204
20.5%

T-RUNS
Real number (ℝ)

ZEROS 

Distinct103
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2166.7154
Minimum0
Maximum15470
Zeros28
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:46.536694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125.5
median542.5
Q33002.25
95-th percentile9111.55
Maximum15470
Range15470
Interquartile range (IQR)2976.75

Descriptive statistics

Standard deviation3305.6468
Coefficient of variation (CV)1.5256488
Kurtosis3.3745254
Mean2166.7154
Median Absolute Deviation (MAD)542.5
Skewness1.9280245
Sum281673
Variance10927300
MonotonicityNot monotonic
2024-03-19T19:37:46.646530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
21.5%
1105 1
 
0.8%
8178 1
 
0.8%
5842 1
 
0.8%
9382 1
 
0.8%
74 1
 
0.8%
1269 1
 
0.8%
710 1
 
0.8%
13 1
 
0.8%
13218 1
 
0.8%
Other values (93) 93
71.5%
ValueCountFrequency (%)
0 28
21.5%
5 1
 
0.8%
11 1
 
0.8%
13 1
 
0.8%
16 1
 
0.8%
17 1
 
0.8%
51 1
 
0.8%
54 1
 
0.8%
58 1
 
0.8%
60 1
 
0.8%
ValueCountFrequency (%)
15470 1
0.8%
13288 1
0.8%
13218 1
0.8%
12379 1
0.8%
10440 1
0.8%
9918 1
0.8%
9382 1
0.8%
8781 1
0.8%
8625 1
0.8%
8178 1
0.8%

T-WKTS
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.530769
Minimum0
Maximum800
Zeros45
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:46.755824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q347.5
95-th percentile376.05
Maximum800
Range800
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation142.67686
Coefficient of variation (CV)2.1445244
Kurtosis10.233566
Mean66.530769
Median Absolute Deviation (MAD)7
Skewness3.1049045
Sum8649
Variance20356.685
MonotonicityNot monotonic
2024-03-19T19:37:46.866048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45
34.6%
1 6
 
4.6%
2 5
 
3.8%
5 5
 
3.8%
7 4
 
3.1%
9 4
 
3.1%
157 2
 
1.5%
6 2
 
1.5%
24 2
 
1.5%
21 2
 
1.5%
Other values (50) 53
40.8%
ValueCountFrequency (%)
0 45
34.6%
1 6
 
4.6%
2 5
 
3.8%
3 1
 
0.8%
5 5
 
3.8%
6 2
 
1.5%
7 4
 
3.1%
8 1
 
0.8%
9 4
 
3.1%
11 1
 
0.8%
ValueCountFrequency (%)
800 1
0.8%
708 1
0.8%
619 1
0.8%
563 1
0.8%
421 1
0.8%
406 1
0.8%
390 1
0.8%
359 1
0.8%
355 1
0.8%
310 1
0.8%

ODI-RUNS-S
Real number (ℝ)

ZEROS 

Distinct117
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2508.7385
Minimum0
Maximum18426
Zeros9
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:46.976065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173.25
median835
Q33523.5
95-th percentile10540.2
Maximum18426
Range18426
Interquartile range (IQR)3450.25

Descriptive statistics

Standard deviation3582.2056
Coefficient of variation (CV)1.4278912
Kurtosis3.4969394
Mean2508.7385
Median Absolute Deviation (MAD)817
Skewness1.8741774
Sum326136
Variance12832197
MonotonicityNot monotonic
2024-03-19T19:37:47.085843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
6.9%
1 3
 
2.3%
18 2
 
1.5%
1100 2
 
1.5%
73 2
 
1.5%
1961 1
 
0.8%
35 1
 
0.8%
47 1
 
0.8%
7040 1
 
0.8%
8090 1
 
0.8%
Other values (107) 107
82.3%
ValueCountFrequency (%)
0 9
6.9%
1 3
 
2.3%
3 1
 
0.8%
4 1
 
0.8%
5 1
 
0.8%
10 1
 
0.8%
18 2
 
1.5%
34 1
 
0.8%
35 1
 
0.8%
38 1
 
0.8%
ValueCountFrequency (%)
18426 1
0.8%
13704 1
0.8%
13430 1
0.8%
11498 1
0.8%
11363 1
0.8%
10889 1
0.8%
10596 1
0.8%
10472 1
0.8%
9619 1
0.8%
8778 1
0.8%

ODI-SR-B
Real number (ℝ)

ZEROS 

Distinct118
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.164385
Minimum0
Maximum116.66
Zeros9
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:47.180153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q165.65
median78.225
Q386.79
95-th percentile98.767
Maximum116.66
Range116.66
Interquartile range (IQR)21.14

Descriptive statistics

Standard deviation25.89844
Coefficient of variation (CV)0.36392417
Kurtosis1.8760155
Mean71.164385
Median Absolute Deviation (MAD)9.32
Skewness-1.4381798
Sum9251.37
Variance670.72918
MonotonicityNot monotonic
2024-03-19T19:37:47.289812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
6.9%
60 2
 
1.5%
50 2
 
1.5%
78.96 2
 
1.5%
100 2
 
1.5%
80.48 1
 
0.8%
86.69 1
 
0.8%
116.66 1
 
0.8%
34.05 1
 
0.8%
113.87 1
 
0.8%
Other values (108) 108
83.1%
ValueCountFrequency (%)
0 9
6.9%
14.28 1
 
0.8%
27.77 1
 
0.8%
34 1
 
0.8%
34.05 1
 
0.8%
36.36 1
 
0.8%
42.97 1
 
0.8%
43.47 1
 
0.8%
43.61 1
 
0.8%
43.87 1
 
0.8%
ValueCountFrequency (%)
116.66 1
0.8%
113.87 1
0.8%
113.6 1
0.8%
104.68 1
0.8%
100.25 1
0.8%
100 2
1.5%
97.26 1
0.8%
96.94 1
0.8%
95.81 1
0.8%
95.33 1
0.8%

ODI-WKTS
Real number (ℝ)

ZEROS 

Distinct74
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.076923
Minimum0
Maximum534
Zeros34
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:47.399724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.5
Q3106
95-th percentile330.7
Maximum534
Range534
Interquartile range (IQR)106

Descriptive statistics

Standard deviation111.20507
Coefficient of variation (CV)1.4617451
Kurtosis2.8036274
Mean76.076923
Median Absolute Deviation (MAD)18.5
Skewness1.7977474
Sum9890
Variance12366.568
MonotonicityNot monotonic
2024-03-19T19:37:47.509612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
26.2%
1 9
 
6.9%
2 4
 
3.1%
3 4
 
3.1%
34 2
 
1.5%
154 2
 
1.5%
185 2
 
1.5%
16 2
 
1.5%
133 2
 
1.5%
7 2
 
1.5%
Other values (64) 67
51.5%
ValueCountFrequency (%)
0 34
26.2%
1 9
 
6.9%
2 4
 
3.1%
3 4
 
3.1%
4 1
 
0.8%
5 1
 
0.8%
7 2
 
1.5%
8 1
 
0.8%
11 1
 
0.8%
12 1
 
0.8%
ValueCountFrequency (%)
534 1
0.8%
400 1
0.8%
393 1
0.8%
381 1
0.8%
377 1
0.8%
344 1
0.8%
337 1
0.8%
323 1
0.8%
293 1
0.8%
288 1
0.8%

ODI-SR-BL
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.033846
Minimum0
Maximum150
Zeros34
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:47.603914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36.6
Q345.325
95-th percentile64.74
Maximum150
Range150
Interquartile range (IQR)45.325

Descriptive statistics

Standard deviation26.751749
Coefficient of variation (CV)0.78603366
Kurtosis3.5277339
Mean34.033846
Median Absolute Deviation (MAD)9.5
Skewness1.0606274
Sum4424.4
Variance715.65605
MonotonicityNot monotonic
2024-03-19T19:37:47.713909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
26.2%
42 3
 
2.3%
47.6 2
 
1.5%
39.4 2
 
1.5%
44.6 2
 
1.5%
36.3 2
 
1.5%
45.1 2
 
1.5%
36.6 2
 
1.5%
41.4 2
 
1.5%
60 2
 
1.5%
Other values (72) 77
59.2%
ValueCountFrequency (%)
0 34
26.2%
12 1
 
0.8%
23.4 1
 
0.8%
27.5 1
 
0.8%
28.5 1
 
0.8%
29 1
 
0.8%
29.2 1
 
0.8%
31.1 1
 
0.8%
31.3 1
 
0.8%
31.4 1
 
0.8%
ValueCountFrequency (%)
150 1
0.8%
137 1
0.8%
117 1
0.8%
90 1
0.8%
86.6 1
0.8%
83.1 1
0.8%
66 1
0.8%
63.2 1
0.8%
61.9 1
0.8%
61.4 1
0.8%

CAPTAINCY EXP
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
89 
1
41 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 89
68.5%
1 41
31.5%

Length

2024-03-19T19:37:47.823747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-19T19:37:47.886770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 89
68.5%
1 41
31.5%

Most occurring characters

ValueCountFrequency (%)
0 89
68.5%
1 41
31.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89
68.5%
1 41
31.5%

Most occurring scripts

ValueCountFrequency (%)
Common 130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89
68.5%
1 41
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89
68.5%
1 41
31.5%

RUNS-S
Real number (ℝ)

ZEROS 

Distinct115
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.24615
Minimum0
Maximum2254
Zeros2
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:47.980558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.45
Q139
median172
Q3925.25
95-th percentile1794.1
Maximum2254
Range2254
Interquartile range (IQR)886.25

Descriptive statistics

Standard deviation615.22633
Coefficient of variation (CV)1.1963655
Kurtosis0.083080232
Mean514.24615
Median Absolute Deviation (MAD)165.5
Skewness1.123952
Sum66852
Variance378503.44
MonotonicityNot monotonic
2024-03-19T19:37:48.090359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 4
 
3.1%
3 4
 
3.1%
39 3
 
2.3%
11 3
 
2.3%
0 2
 
1.5%
49 2
 
1.5%
36 2
 
1.5%
52 2
 
1.5%
81 2
 
1.5%
137 1
 
0.8%
Other values (105) 105
80.8%
ValueCountFrequency (%)
0 2
1.5%
2 1
 
0.8%
3 4
3.1%
4 4
3.1%
6 1
 
0.8%
7 1
 
0.8%
8 1
 
0.8%
9 1
 
0.8%
10 1
 
0.8%
11 3
2.3%
ValueCountFrequency (%)
2254 1
0.8%
2065 1
0.8%
2047 1
0.8%
1975 1
0.8%
1965 1
0.8%
1879 1
0.8%
1804 1
0.8%
1782 1
0.8%
1775 1
0.8%
1703 1
0.8%

HS
Real number (ℝ)

ZEROS 

Distinct73
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.430769
Minimum0
Maximum158
Zeros2
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:48.184542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median35.5
Q373.75
95-th percentile109.55
Maximum158
Range158
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation36.403624
Coefficient of variation (CV)0.76751073
Kurtosis-0.61175506
Mean47.430769
Median Absolute Deviation (MAD)27
Skewness0.58683602
Sum6166
Variance1325.2239
MonotonicityNot monotonic
2024-03-19T19:37:48.309934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 7
 
5.4%
71 5
 
3.8%
16 5
 
3.8%
11 4
 
3.1%
15 4
 
3.1%
24 4
 
3.1%
33 3
 
2.3%
109 3
 
2.3%
23 3
 
2.3%
69 3
 
2.3%
Other values (63) 89
68.5%
ValueCountFrequency (%)
0 2
 
1.5%
2 3
2.3%
3 7
5.4%
4 2
 
1.5%
6 1
 
0.8%
8 2
 
1.5%
9 2
 
1.5%
10 2
 
1.5%
11 4
3.1%
13 2
 
1.5%
ValueCountFrequency (%)
158 1
 
0.8%
128 1
 
0.8%
119 1
 
0.8%
117 1
 
0.8%
116 1
 
0.8%
114 1
 
0.8%
110 1
 
0.8%
109 3
2.3%
105 1
 
0.8%
103 1
 
0.8%

AVE
Real number (ℝ)

ZEROS 

Distinct113
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.719308
Minimum0
Maximum50.11
Zeros3
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:48.419714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.45
Q19.825
median18.635
Q327.8725
95-th percentile36.1525
Maximum50.11
Range50.11
Interquartile range (IQR)18.0475

Descriptive statistics

Standard deviation11.094224
Coefficient of variation (CV)0.5926621
Kurtosis-0.81045247
Mean18.719308
Median Absolute Deviation (MAD)9.08
Skewness0.14752109
Sum2433.51
Variance123.08181
MonotonicityNot monotonic
2024-03-19T19:37:48.529509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 4
 
3.1%
0 3
 
2.3%
11 3
 
2.3%
3 3
 
2.3%
9 3
 
2.3%
27.97 2
 
1.5%
22.33 2
 
1.5%
1 2
 
1.5%
13 2
 
1.5%
21 2
 
1.5%
Other values (103) 104
80.0%
ValueCountFrequency (%)
0 3
2.3%
1 2
1.5%
1.5 1
 
0.8%
2 1
 
0.8%
3 3
2.3%
3.33 1
 
0.8%
3.47 1
 
0.8%
3.5 1
 
0.8%
4 4
3.1%
4.33 1
 
0.8%
ValueCountFrequency (%)
50.11 1
0.8%
42.27 1
0.8%
39.92 1
0.8%
37.91 1
0.8%
37.13 1
0.8%
36.9 1
0.8%
36.22 1
0.8%
36.07 1
0.8%
34.26 1
0.8%
33.64 1
0.8%

SR-B
Real number (ℝ)

ZEROS 

Distinct125
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.05346
Minimum0
Maximum235.49
Zeros2
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:48.639358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.614
Q198.2375
median118.51
Q3129.1025
95-th percentile158.2125
Maximum235.49
Range235.49
Interquartile range (IQR)30.865

Descriptive statistics

Standard deviation35.928907
Coefficient of variation (CV)0.32352802
Kurtosis2.2680893
Mean111.05346
Median Absolute Deviation (MAD)13.63
Skewness-0.58987438
Sum14436.95
Variance1290.8863
MonotonicityNot monotonic
2024-03-19T19:37:48.733592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.5%
80 2
 
1.5%
131.88 2
 
1.5%
133.33 2
 
1.5%
110.63 2
 
1.5%
132.09 1
 
0.8%
149.25 1
 
0.8%
176.08 1
 
0.8%
167.32 1
 
0.8%
97.33 1
 
0.8%
Other values (115) 115
88.5%
ValueCountFrequency (%)
0 2
1.5%
0.75 1
0.8%
28.57 1
0.8%
30.3 1
0.8%
30.77 1
0.8%
33.33 1
0.8%
42.85 1
0.8%
50 1
0.8%
58.33 1
0.8%
60 1
0.8%
ValueCountFrequency (%)
235.49 1
0.8%
205.26 1
0.8%
176.08 1
0.8%
167.32 1
0.8%
165.88 1
0.8%
164.1 1
0.8%
161.79 1
0.8%
153.84 1
0.8%
151.41 1
0.8%
149.25 1
0.8%

SIXERS
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.692308
Minimum0
Maximum129
Zeros26
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:48.843588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q329.75
95-th percentile65.65
Maximum129
Range129
Interquartile range (IQR)28.75

Descriptive statistics

Standard deviation23.828146
Coefficient of variation (CV)1.3468083
Kurtosis4.1984324
Mean17.692308
Median Absolute Deviation (MAD)6
Skewness1.889971
Sum2300
Variance567.78056
MonotonicityNot monotonic
2024-03-19T19:37:48.953358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 26
20.0%
1 18
 
13.8%
3 7
 
5.4%
5 6
 
4.6%
6 5
 
3.8%
2 5
 
3.8%
8 4
 
3.1%
9 3
 
2.3%
24 3
 
2.3%
28 3
 
2.3%
Other values (38) 50
38.5%
ValueCountFrequency (%)
0 26
20.0%
1 18
13.8%
2 5
 
3.8%
3 7
 
5.4%
4 2
 
1.5%
5 6
 
4.6%
6 5
 
3.8%
8 4
 
3.1%
9 3
 
2.3%
11 1
 
0.8%
ValueCountFrequency (%)
129 1
0.8%
97 1
0.8%
86 1
0.8%
82 1
0.8%
81 1
0.8%
79 1
0.8%
67 1
0.8%
64 1
0.8%
59 1
0.8%
50 1
0.8%

RUNS-C
Real number (ℝ)

ZEROS 

Distinct92
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475.52308
Minimum0
Maximum1975
Zeros36
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:49.047735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median297
Q3689.25
95-th percentile1690.95
Maximum1975
Range1975
Interquartile range (IQR)689.25

Descriptive statistics

Standard deviation558.31405
Coefficient of variation (CV)1.1741051
Kurtosis0.30618705
Mean475.52308
Median Absolute Deviation (MAD)297
Skewness1.195525
Sum61818
Variance311714.58
MonotonicityNot monotonic
2024-03-19T19:37:49.166821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
27.7%
40 2
 
1.5%
105 2
 
1.5%
345 2
 
1.5%
307 1
 
0.8%
303 1
 
0.8%
85 1
 
0.8%
54 1
 
0.8%
408 1
 
0.8%
419 1
 
0.8%
Other values (82) 82
63.1%
ValueCountFrequency (%)
0 36
27.7%
21 1
 
0.8%
24 1
 
0.8%
29 1
 
0.8%
40 2
 
1.5%
45 1
 
0.8%
54 1
 
0.8%
58 1
 
0.8%
66 1
 
0.8%
70 1
 
0.8%
ValueCountFrequency (%)
1975 1
0.8%
1919 1
0.8%
1899 1
0.8%
1892 1
0.8%
1819 1
0.8%
1783 1
0.8%
1713 1
0.8%
1664 1
0.8%
1548 1
0.8%
1530 1
0.8%

WKTS
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.169231
Minimum0
Maximum83
Zeros41
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:49.267970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.5
Q323.75
95-th percentile67.65
Maximum83
Range83
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation21.816763
Coefficient of variation (CV)1.2706896
Kurtosis0.81117362
Mean17.169231
Median Absolute Deviation (MAD)8.5
Skewness1.3762898
Sum2232
Variance475.97114
MonotonicityNot monotonic
2024-03-19T19:37:49.377738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
31.5%
6 5
 
3.8%
9 4
 
3.1%
13 4
 
3.1%
2 4
 
3.1%
36 3
 
2.3%
12 3
 
2.3%
10 3
 
2.3%
5 3
 
2.3%
8 3
 
2.3%
Other values (41) 57
43.8%
ValueCountFrequency (%)
0 41
31.5%
1 3
 
2.3%
2 4
 
3.1%
3 1
 
0.8%
4 2
 
1.5%
5 3
 
2.3%
6 5
 
3.8%
7 3
 
2.3%
8 3
 
2.3%
9 4
 
3.1%
ValueCountFrequency (%)
83 1
0.8%
74 2
1.5%
73 1
0.8%
70 1
0.8%
69 2
1.5%
66 1
0.8%
65 1
0.8%
61 1
0.8%
59 1
0.8%
57 2
1.5%

AVE-BL
Real number (ℝ)

ZEROS 

Distinct88
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.110231
Minimum0
Maximum126.3
Zeros41
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:49.471878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24.785
Q335.58
95-th percentile52.5
Maximum126.3
Range126.3
Interquartile range (IQR)35.58

Descriptive statistics

Standard deviation20.802057
Coefficient of variation (CV)0.9001233
Kurtosis3.8629075
Mean23.110231
Median Absolute Deviation (MAD)13.005
Skewness1.2032211
Sum3004.33
Variance432.72559
MonotonicityNot monotonic
2024-03-19T19:37:49.581642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
31.5%
40 2
 
1.5%
52.5 2
 
1.5%
30.71 1
 
0.8%
32.67 1
 
0.8%
25 1
 
0.8%
37.67 1
 
0.8%
37.88 1
 
0.8%
33.9 1
 
0.8%
40.54 1
 
0.8%
Other values (78) 78
60.0%
ValueCountFrequency (%)
0 41
31.5%
10.8 1
 
0.8%
12.09 1
 
0.8%
14.38 1
 
0.8%
15.33 1
 
0.8%
16.5 1
 
0.8%
16.64 1
 
0.8%
17.42 1
 
0.8%
17.53 1
 
0.8%
17.65 1
 
0.8%
ValueCountFrequency (%)
126.3 1
0.8%
86.25 1
0.8%
72.5 1
0.8%
71.2 1
0.8%
57.5 1
0.8%
53.5 1
0.8%
52.5 2
1.5%
49.5 1
0.8%
49.17 1
0.8%
48.24 1
0.8%

ECON
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2044615
Minimum0
Maximum38.11
Zeros36
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:49.691632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.38
Q38.2475
95-th percentile10.308
Maximum38.11
Range38.11
Interquartile range (IQR)8.2475

Descriptive statistics

Standard deviation4.9415305
Coefficient of variation (CV)0.79644793
Kurtosis12.597887
Mean6.2044615
Median Absolute Deviation (MAD)1.095
Skewness1.9180961
Sum806.58
Variance24.418724
MonotonicityNot monotonic
2024-03-19T19:37:49.801396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
27.7%
8.25 3
 
2.3%
10 2
 
1.5%
7.96 2
 
1.5%
7.73 2
 
1.5%
7.71 2
 
1.5%
7.89 2
 
1.5%
9.55 2
 
1.5%
7.02 2
 
1.5%
7.11 2
 
1.5%
Other values (73) 75
57.7%
ValueCountFrequency (%)
0 36
27.7%
6.23 1
 
0.8%
6.36 1
 
0.8%
6.46 1
 
0.8%
6.49 1
 
0.8%
6.54 1
 
0.8%
6.58 1
 
0.8%
6.6 1
 
0.8%
6.61 1
 
0.8%
6.66 1
 
0.8%
ValueCountFrequency (%)
38.11 1
0.8%
21 1
0.8%
14.5 1
0.8%
14 1
0.8%
12 1
0.8%
11.25 1
0.8%
10.56 1
0.8%
10 2
1.5%
9.89 1
0.8%
9.88 1
0.8%

SR-BL
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.382615
Minimum0
Maximum100.2
Zeros41
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:49.911237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19.935
Q326.2125
95-th percentile37.232
Maximum100.2
Range100.2
Interquartile range (IQR)26.2125

Descriptive statistics

Standard deviation15.273422
Coefficient of variation (CV)0.87866074
Kurtosis5.5035339
Mean17.382615
Median Absolute Deviation (MAD)8.455
Skewness1.2943316
Sum2259.74
Variance233.27741
MonotonicityNot monotonic
2024-03-19T19:37:50.021153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
31.5%
24 3
 
2.3%
33 3
 
2.3%
27 2
 
1.5%
28.11 2
 
1.5%
12 2
 
1.5%
11.2 2
 
1.5%
26.36 1
 
0.8%
29.5 1
 
0.8%
28.9 1
 
0.8%
Other values (72) 72
55.4%
ValueCountFrequency (%)
0 41
31.5%
8.4 1
 
0.8%
11.2 2
 
1.5%
12 2
 
1.5%
13.41 1
 
0.8%
13.76 1
 
0.8%
13.93 1
 
0.8%
14.95 1
 
0.8%
15.56 1
 
0.8%
15.57 1
 
0.8%
ValueCountFrequency (%)
100.2 1
0.8%
58.5 1
0.8%
53 1
0.8%
44 1
0.8%
41.33 1
0.8%
39 1
0.8%
38.24 1
0.8%
36 1
0.8%
34.85 1
0.8%
34.83 1
0.8%

SOLD PRICE
Real number (ℝ)

Distinct53
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521223.08
Minimum20000
Maximum1800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-19T19:37:50.115293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile50000
Q1225000
median437500
Q3700000
95-th percentile1527500
Maximum1800000
Range1780000
Interquartile range (IQR)475000

Descriptive statistics

Standard deviation406807.35
Coefficient of variation (CV)0.78048607
Kurtosis2.0045148
Mean521223.08
Median Absolute Deviation (MAD)237500
Skewness1.3769563
Sum67759000
Variance1.6549222 × 1011
MonotonicityNot monotonic
2024-03-19T19:37:50.225297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 6
 
4.6%
100000 6
 
4.6%
500000 6
 
4.6%
200000 5
 
3.8%
350000 5
 
3.8%
150000 5
 
3.8%
675000 5
 
3.8%
800000 5
 
3.8%
700000 5
 
3.8%
300000 4
 
3.1%
Other values (43) 78
60.0%
ValueCountFrequency (%)
20000 1
 
0.8%
24000 1
 
0.8%
50000 6
4.6%
80000 1
 
0.8%
95000 1
 
0.8%
100000 6
4.6%
110000 1
 
0.8%
125000 1
 
0.8%
140000 1
 
0.8%
150000 5
3.8%
ValueCountFrequency (%)
1800000 4
3.1%
1600000 1
 
0.8%
1550000 2
1.5%
1500000 1
 
0.8%
1350000 1
 
0.8%
1000000 1
 
0.8%
975000 1
 
0.8%
950000 4
3.1%
925000 1
 
0.8%
900000 2
1.5%

Interactions

2024-03-19T19:37:43.360630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.246384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.494994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.742361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.032581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.245230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.407084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.743762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.245205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.495487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.679788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.811253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.994471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.647582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.777053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.967282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.209243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.443261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.331325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.579089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.812903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.114179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.327281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.485701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.810482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.329685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.561419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.751299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.902536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.081077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.710897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.862219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.052086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.276462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.526841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.402039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.662108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.895988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.180104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.395592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.564495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.877170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.406067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.628146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.822980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.974790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.160019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.792819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.929646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.128678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.359190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.610073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.477622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.729360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.962008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.269614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.460749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.636237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.961256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.487332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.710303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.894697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.043724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.227628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.860968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.010617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.198375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.427000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.676685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.549474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.796428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:25.030747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.346603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.534400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.709182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.031427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.560083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.777837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.945053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.109772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.309897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.926275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.061176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.277004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.493551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.743495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.612536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.862608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:25.113608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.411811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.593921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.787710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.413186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.627714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.847035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.011358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.184296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.376495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.992914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.143879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.342710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.560587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.826737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.694769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.952669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.197717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.484911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.661538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.897484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.480093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.694504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.920715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.092598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.244863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.826658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.061419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.209219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.410352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.629403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.896135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.770278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.012567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.266551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.545862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.734832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.988472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.546983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.761225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.983107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.144936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.310785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.909864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.110594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.277190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.493235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.693572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.960630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.846124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.095726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.331996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.611807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.796584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.077649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.611152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.847510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.055864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.226804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.392291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:37.992554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.196253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.349962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.563541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.760686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.042259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.911207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.162267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.413242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.678521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.859638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.158563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.678584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.918069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.121637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.277814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.444888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.064377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.260078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.410376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.630468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.834654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.109436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:22.978979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.229999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.479910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.745180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.934964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.221082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.759612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.983742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.183474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.352250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.521672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.130928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.310681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.477077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.703123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.893375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.180789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.069490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.304711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.546375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.825162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.994689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.283997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.826954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.055338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.253784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.411113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.577612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.209749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.376638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.551616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.761064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.959437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.261727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.146174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.379135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.646312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.892041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.060738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.372861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.893150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.127667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.327279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.477944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.660994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.277120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.449220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.626265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.843562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.030317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.331974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.212939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.445161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.708888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:27.954929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.135230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.437433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:31.959556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.195022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.394828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.552157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.726592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.347099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.517397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.677095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.910120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.099196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.393288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.278760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.518405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.771710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.028482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.200415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.511287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.027347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.261358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.461392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.611195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.793421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.426500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.577485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.751292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:41.993671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.160942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.476806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.362362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.592639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.863879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.095144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.279032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.594729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.095170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.345234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.528428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.686526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.860409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.492987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.652508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.827188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.060091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.237545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:44.552830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:23.430342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:24.661665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:26.946420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:28.161744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:29.344580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:30.661486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:32.160764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:33.411146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:34.595262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:35.744777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:36.927973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:38.561670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:39.716377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:40.893546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:42.126848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-19T19:37:43.293369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-03-19T19:37:44.684124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-19T19:37:44.891143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PLAYER NAMEAGECOUNTRYPLAYING ROLET-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLCAPTAINCY EXPRUNS-SHSAVESR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLSOLD PRICE
0Abdulla, YA2SAAllrounder0000.0000.00000.000.0003071520.478.9013.9350000
1Abdur Razzak2BANBowler2141865771.4118537.60000.000.0002900.0014.500.0050000
2Agarkar, AB2INDBowler57158126980.6228832.901673918.56121.01510592936.528.8124.90350000
3Ashwin, R1INDBowler2843124184.565136.8058115.8076.32011254922.966.2322.14850000
4Badrinath, S2INDBatsman6307945.9300.0013177132.93120.7128000.000.000.00800000
5Bailey, GJ2AUSBatsman0017272.2600.01634821.0095.450000.000.000.0050000
6Balaji, L2INDBowler512712078.943442.5026154.3372.22113425225.817.9819.40500000
7Bollinger, DE2AUSBowler54505092.596231.30211621.00165.8816933718.737.2215.57700000
8Botha, J2SAAllrounder831760985.777253.013356730.45114.7336101932.116.8528.11950000
9Boucher, MV2SAW. Keeper55151468684.7600.013945028.14127.5113000.000.000.00450000
PLAYER NAMEAGECOUNTRYPLAYING ROLET-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLCAPTAINCY EXPRUNS-SHSAVESR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLSOLD PRICE
120Vettori, DL2NZAllrounder4486359210581.9328245.711212915.13107.0828782831.366.8127.75625000
121Vinay Kumar, R2INDBowler1114343.872835.30217259.43104.83516646127.288.2419.87475000
122Warne, SK3AUSBowler3154708101872.0429336.31198349.9092.52614475725.397.2720.95450000
123Warner, DA1AUSBatsman483287685.7900.00102510927.70135.7644000.000.000.00750000
124White, CL2AUSBatsman1465203780.481227.517457831.04132.09297000.0014.000.00500000
125Yadav, AS2INDBatsman0000.0000.0049169.80125.642000.000.000.00750000
126Younis Khan2PAKBatsman63987681475.78386.61333.0042.850000.000.000.00225000
127Yuvraj Singh2INDBatsman17759805187.5810944.3112376626.32131.88675692324.747.0221.131800000
128Zaheer Khan2INDBowler111428879073.5527835.4099239.9091.67117836527.437.7521.26450000
129Zoysa, DNT2SLBowler2886434395.8110839.40111011.00122.22099249.509.0033.00110000